Contextual data: Tanzania - Maize - All years

‹ 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 ›
Tanzania total 2,223,870 2,201,221 2,376,662 2,363,028 2,486,198 3,337,259 4,347,180 4,549,226 4,482,033 5,175,920
Arusha   98,898 21,496 43,553 45,831 36,214 29,554 145,263 109,307 86,783 150,425  
Dar-Es-Salaam   646 631 818 979 773 3,134 554 3,060 1,421 2,191  
Dodoma   143,117 43,290 50,014 140,580 153,830 65,956 63,142 62,569 70,937 68,716  
Geita   - - - - - - - - - -  
Iringa   258,083 292,360 288,732 442,019 483,682 624,786 538,838 359,157 639,421 636,409  
Kagera   134,413 178,021 192,991 135,425 107,006 173,773 171,818 197,806 306,355 278,482  
Kaskazini Pemba   - - - - - - - - - -  
Kaskazini Unguja   - - - - - - - - - -  
Katavi   - - - - - - - - - -  
Kigoma   28,949 17,019 22,335 19,680 15,551 119,843 267,923 524,108 159,612 421,103  
Kilimanjaro   48,172 70,447 86,375 60,079 37,492 59,396 97,722 61,015 119,814 121,026  
Kusini Pemba   - - - - - - - - - -  
Kusini Unguja   - - - - - - - - - -  
Lindi   114,602 47,368 64,593 69,739 76,313 21,515 32,557 38,618 40,507 41,150  
Manyara   247,246 204,704 235,871 240,340 262,994 108,540 245,751 245,675 171,950 178,032  
Mara   55,638 46,146 46,969 35,306 27,897 71,820 68,127 64,311 158,898 166,594  
Mbeya   17,612 20,265 19,481 17,559 13,874 442,667 529,785 628,418 659,156 707,779  
Mjini Magharibi   - - - - - - - - - -  
Morogoro   20,414 23,498 23,917 21,161 16,721 146,514 130,568 292,547 306,854 322,593  
Mtwara   75,578 142,279 144,818 154,909 169,511 36,196 160,746 105,673 23,249 24,974  
Mwanza   117,307 87,624 81,981 76,536 50,396 123,208 186,585 221,332 196,246 288,851  
Njombe   - - - - - - - - - -  
Pwani   4,434 3,302 3,688 3,313 2,150 18,143 18,143 94,549 15,562 18,143  
Rukwa   240,572 332,636 338,572 251,960 327,405 519,755 464,369 330,822 523,798 538,291  
Ruvuma   213,653 335,335 341,320 303,636 332,255 392,459 462,385 225,470 423,078 444,388  
Shinyanga   141,501 128,038 180,182 180,182 197,165 197,497 256,067 267,740 208,811 330,923  
Simiyu   - - - - - - - - - -  
Singida   36,408 24,102 24,532 35,894 39,277 39,343 106,001 77,735 50,695 72,960  
Tabora   207,813 163,846 166,770 113,882 124,616 123,395 259,678 308,023 231,515 241,402  
Tanga   18,814 18,814 19,150 14,018 11,076 19,765 141,158 331,291 87,371 121,488  

Data version 2.19.3

Contextual data

Collected data

Values obtained from primary and secondary sources

Approximated data

Values approximated based on other data for same location

–No data

Not applicable or no data available

LGB Larger Grain Borer (Prostephanus truncatus) read more ›

Storage beetle pest of maize and dried cassava

Contextual data

Contextual factors relate to local conditions and practices that may affect losses on a seasonal or annual basis, such as weather, pest incidence, grain drying conditions and the length of household-level storage.

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APHLIS network and team members collect this data from official sources (e.g. ministries of agriculture and statistics offices), published research studies and national surveys etc, or by interviewing farmers, extension workers or other key informants. This data enables APHLIS to apply the relevant loss figures from the PHL profiles depending on seasonal circumstances, and convert percentage losses into absolute losses (in tonnes) using the production figures. Based on the postharvest loss profiles and contextual factors, APHLIS estimates postharvest losses at the provincial, national and regional levels.

The contextual dataset includes production quantities (t), whether it rained at harvest, the percentage of the crop marketed during first 3 months after harvest, household-level storage duration, and presence of specific pests (for example the larger grain borer, LGB, Prostephanus truncatus) which are used to contextualise the loss profile for each year. The importance of different types of contextual factors may vary between crops, and data for the most relevant contextual factors is shown and used in the loss estimate calculations.

For specific crop, location, year combinations where contextual data is not available, approximated data has been used. This is calculated based on the contextualised data which has been provided for the same location. Approximated contextual data is shown in grey. 

Read more about how APHLIS estimates losses ›

Contextual factors

Production quantity (t) the quantity of grain produced on small and large farms for each growing season.

Rain at harvest whether or not there is damp weather at time of harvest which would make drying the grain difficult. If there is rain at harvest then the value for the harvesting link in the PHL profile is increased.

% sold during first 3 months the proportion of grain that is marketed within the first three months, i.e. that will not enter farm storage for any significant time. This proportion of the production will not be subject to farm storage losses but instead will be affected by transport and market storage losses.

Household-level storage duration (months) the length of time that grain was stored at household-level. If household-level storage is less than 3 months then household-level storage loss is reduced to 0%, if 3 to 6 months it will be only 50% of the loss profile figure, or if 6 months or more than 100% of the household-level storage loss is applied.

LGB presence in the case of maize, whether or not LGB (the larger grain borer, Prostephanus truncatus) is expected to be a significant pest. If LGB is a serious pest in that particular season then storage losses are multiplied by 2.

Season

In locations where there is more than one growing season each year, seasonal production data for each of the growing seasons is inputted. These are labelled as first, second, third season.

Farm type

In locations where there are both smallholder farms and large-scale commercial farms, the contextual data is shown for each of these types.

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